To be effective, supervised DNNs rely on large amounts ofcarefully labeled training data. However, it is not always realistic to assume that example labels are clean.
For every token we choose tointroduce the different types of noise with some probability onboth French and English sides in 100k sentences of EP.
To this end为此, we propose two approaches to constructing noisy inputs with small perturbations to make NMT models resist them.
Transparent to network architectures对网络结构透明: Our adversarial stability training does not depend on specific NMT architectures.
our method has the overhead of有什么的开销 sorting or searching among derivatives, while being considerably more successful.
so it is more sensible to什么是明智的only annotate the unknown words
In stark contrast 形成了鲜明的对比
The other side of the coin is to improve另一方便是提高 models’ robustness to adversarial examples
we restrict ourselves to single word replacements 我们仅限于单个词进行替换
For each problem we want to tackle we create a new subclass of Problem
Neural machine translation (NMT) is notoriously sensitive to noises众所周知的对噪声很敏感
there are still several shortcomings of NMT that need to be addressed
This is consistent with previous work, yet orthogonal to it, since we use more realistic noise for our experiments
In line with our findings 与我们的发现一致, they also showed that
Autoencoders areseemingly esoteric看起来深奥
The analogy above上面的类别 is an example of a lossy data compression algorithm
The correctness of segmentation cannot be guaranteed切分的正确性不能保证
It’s a blend of 是...的混合体character and word level encoding.
This is only one dataset, so please take it with a grain of salt可以以怀疑的态度来看.古罗马时期有一种解毒剂难以下咽，人们和着盐才能服下，因此take something with salt表示对感觉不靠谱的东西将信将疑的态度。
T2T, being implemented in用 TensorFlow
youshould not expect to improve the final BLEU这种方法不能获得最终BLEU的提升 this way.
Resumed training can also be exploited for changing 可以被用来 some hyper-parameters
Recurrent Neural Networks have loops in them, allowing informationto persist.允许信息继续存留
But there are cases where we need more context有些情况下我们需要更多的信息
the longer the chain is, the more probable更大的可能性 the information is lost along the chain.
LSTM, a special type of RNN, tries to solve this kind of problem
The reason for that is that the probability of keeping the context from a word that is far away from the current word being processed decreases exponentially with the distance from it.
That means that when sentences are long, the model often forgets the content of distant positions in the sequence.
When translating a sentence, I pay special attention to the word I’m presently translating. 省略了主语和谓语i am
With them we can trivial to我们可以容易的进行并行化处理 parallelize (per layer)
The embedding only happens in the bottom-most只在最下面的 encoder
For people not versed不熟悉in the topic
The first input token is supplied with a special [CLS] tokenfor reasons thatwill become apparent later on.
In terms of architecture在架构方面
OpenAI GPT was unceremoniously knocked off 被bert毫不客气的从GLEU榜单上踢出去the GLUE leaderboard by BERT
OpenAI did not release the full GPT-2 model due to concerns of malicious use对恶意使用的担忧。
Context fragmentation refers to 指的是
I think you might be getting stuck on卡在 the idea that a noun is either abstract or not abstract
Now if we stretch the fiction扩展小说 a little further
It’s time to get rid of 抛弃the black boxes and cultivate trust 培养信任in Machine Learning
Christoph Molnar beautifully encapsulates the essence of将ML可解释性的精髓封装金这个例子 ML interpretability through this example
I kinda有点 agree with Mr. Rahimi on this one
The catch is that 问题在于Alice and Bob are separated from each other and can only communicate in a very limited way through a set of special devices.
While adjusting their process调整他们动作的时候, Alice and Bob need to be careful not to tweak it too much based on a single photo.
And the training paid off!训练得到了回报
The trick,given the limited information flow allowed,鉴于有限的信息流，诀窍在于 lies in Alice encoding exactly the kind of information she thinks is surprising to Bob and letting him rely on his own language model for the rest
But for now, Alice and Bob need a well-deserved应得的，当之无愧的 rest.
Mythbusters流言终结者
The model is now exposed to a certain degree of local variation by varying the encoding of one sample模型通过改变sample的encoding来展现具体程度的局部变化，一个很典型的重要的部分在前修饰的部分在后的结构。
so it is kosher to minibatch our data 使用小batch是合理的
We fix the values of the latent variables to be equally spaced between -3 and 3将什么固定在什么的范围
We need to decide on就什么做出决定 the language used for discussing variational autoencodersin a clear and concise way 英语就是这样先说重要的然后进行补充
taxonomy，分类学分类系统
Please don’t hesitate to let us know for any additional comments on the paper or on the planned changes.
For tasks that require specific information not captured by the contextual word representation
The recent poster child of this trend 典范is the deep language representation model.
Given the ever-changing不断变化的 environment of products and services
In order to make the argument more convincing为了更加让人信服, the authors should explicitly describe the amount of memory consumed by each algorithms.
The proposed approach is neither well motivated, nor well presented/justified. 提出的方法既没有很好的动机也没有很好的证明
The description of the model is laborious费力的 and hard to follow
The assumption given in the introduction is that softmax would not yield such a representation, but nowhere in the paper this assumption is verified但是文中没有证实这个假设 but nowhere in the paper.
The paper also presents detailed proofs详尽的证明 that there exists a convex optimization problem for which the ADAM regret does not converge to zero.
I detail my key criticisms below我在下面详述我的主要批评
the code is a little bit messy currently代码现在还有点乱.We are cleaning up the code for releasing it these days.
to develop an architecture capable of learning continuously from sequentially incoming tasks, while averting catastrophic forgetting同事避免灾难性遗忘.
The big-picture idea全局idea is fairly simple
Higher education in China has gone from strength to strength不断壮大 in recent years.
In terms of life outside of studying 在课外学习方面, the university has more than 110 student associations covering science and technology, physical training, humanities, arts and public welfare.
This is a typical money making scam诈骗 under the guise伪装 of a 'university'. 这是一个以“大学”为幌子的典型赚钱骗局。
- So I thought, why not write an article
on itfor those who are familiar with neural networkonly at a basic levelandis therefore,`` wondering about寻思着activation functions and their “why-how-mathematics!”. -
It is a great way to get an idea of 了解什么的好方法the different styles of writing andsee明白how to use words appropriately.
Deep neural networks (DNNs) arestrikingly令人吃惊的susceptible to minimal
One key probleminfinding successful defensesis the difficulty of reliably evaluating可靠的评估model robustness.
It has been shown time and again 说出一个观点然后举出很多引用(Athalye et al., 2018; Athalye & Carlini, 2018; Brendel &
Bethge, 2017)
The few verifiable defenses can only
guarantee robustnesswithin a small linear regime around the data points在小的线性区域内保证
Second, the robustness resultsbyMadry
et al. can also be achieved
make little to no sense to humans.对人类有很小或者没有意义
Taken together,综合来看even MNIST cannotbe considered solved被认为已解决with respect to adversarial robustness
gauge评估their confidence accordingly.
Byadditionally learning额外的学习the image distributionwithin每一个标签内部的分布each class we can check
Following this line of thought from an information-theoretic perspective,从信息学的角度来看，onearrives at the well-known concept of Bayesian classifiers这就提出了贝叶斯分类器的概念
perturbations that aremeaningless to对人类来说毫无意义humans.
In summary, thecontributionsof this paper areas follows: - Unlike previous methods that use random noise such as Gaussian noise or dropout noise,
UDAhas a small twist in that 说明和之前方法的不同itmakes use of利用harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to
substantial improvements on six language tasks. - Finally, we also find UDA
to be beneficial什么 时候会是有用的when there is a large amount of supervised data. - Fourth, our paper show
significant leaps显著的提升in performancecompared to 与之前的方法相比previous methodsin a range ofvision and languagetasks.
realistic-looking training data看起来像真的训练数据 - This objective
can be equivalently seen as可以被看成constructing an augmented labeled set from the original supervised set and then training the model on the augmented set. - Employing the back-translation based augmentation has
led to significant performance improvements - outperforming any
manually designed augmentation procedure手动设计的数据增强方法by a clear margin. - Despite the promising results, data augmentation is mostly regarded as the
“cherry on the cake”锦上添花which provides asteady but limited稳定但有限的提升performance boost because these augmentations has so far only been applied to a set of labeled examples which is usually small. - As discussed in the introduction,
a recent line of work近期的一系列工作in semi-supervised learning has been utilizing unlabeled examples to enforce smoothness of the model. - The results suggest that our approach
yieldsstate of the art robustness on MNIST against L0 L2 and L1 perturbations and we demonstrate that